PTeacher: a Computer-Aided Personalized Pronunciation Training System with Exaggerated Audio-Visual Corrective Feedback
Yaohua Bu, Tianyi Ma, Weijun Li, Hang Zhou, Jia Jia, Shengqi Chen, Kaiyuan Xu, Dachuan Shi, Haozhe Wu, Zhihan Yang, Kun Li, Zhiyong Wu
Abstract
Second language (L2) English learners often find it difficult to improve their pronunciations due to the lack of expressive and personalized corrective feedback. In this paper, we present Pronunciation Teacher (PTeacher), a Computer-Aided Pronunciation Training (CAPT) system that provides personalized exaggerated audio-visual corrective feedback for mispronunciations. Though the effectiveness of exaggerated feedback has been demonstrated, it is still unclear how to define the appropriate degrees of exaggeration when interacting with individual learners. To fill in this gap, we interview 100 L2 English learners and 22 professional native teachers to understand their needs and experiences. Three critical metrics are proposed for both learners and teachers to identify the best exaggeration levels in both audio and visual modalities. Additionally, we incorporate the personalized dynamic feedback mechanism given the English proficiency of learners. Based on the obtained insights, a comprehensive interactive pronunciation training course is designed to help L2 learners rectify mispronunciations in a more perceptible, understandable, and discriminative manner. Extensive user studies demonstrate that our system significantly promotes the learners’ learning efficiency.
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Your agent calls
Luneget_paper_fulltext
Free to start. No credit card required.
Terminal
Install the CLIlune papers fulltext 25342d79-7694-46fe-a926-ff303152d93bCited by top-tier papers1
Ask how each one uses itRelated papers
- WithYou: Automated Adaptive Speech Tutoring With Context-Dependent Speech RecognitionXinlei Zhang, Takashi Miyaki, Jun RekimotoCHI 2020 · 17 citations
- Designing CAST: A Computer-Assisted Shadowing Trainer for Self-Regulated Foreign Language Listening PracticeMohi Reza, Dongwook YoonCHI 2021 · 4 citations
- Toward Automated Feedback on Teacher Discourse to Enhance Teacher LearningEmily Jensen, Meghan Dale, Patrick J. Donnelly, Cathlyn Stone et al.CHI 2020 · 90 citations
- An Effective Pronunciation Assessment Approach Leveraging Hierarchical Transformers and Pre-training StrategiesBi-Cheng Yan, Jiun-Ting Li, Yi-Cheng Wang, Hsin-Wei Wang et al.ACL 2024
- Error-preserving Automatic Speech Recognition of Young English Learners' LanguageJanick Michot, Manuela Hürlimann, Jan Deriu, Luzia Sauer et al.ACL 2024 · 2 citations
